Incident site investigation and management support system based on unmanned aerial vehicles

ABSTRACT

Systems and methods allow for incident data collection and management system based on unmanned aerial vehicles (UAVs), that is, drones to help accelerate the data collection and analytics, information dissemination, and decision support at incident sites. The system architecture may include onsite, server, and offline components including flight planning subsystem, flight execution and mission control subsystem, information dissemination subsystem to travelers and traveler information services, the interface with traffic management center, and the data analytic, visualization, and training subsystems. Other embodiments include the video-based 3D incident site reconstruction methods, site positioning and scaling methods with pre-collected static background infrastructure data, data management and user charging methods, and training methods with the generated 3D model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/600,212, filed Oct. 11, 2019, which claims the benefit of U.S.application Ser. No. 16/101,535, filed Aug. 13, 2018, which claims thebenefit of U.S. Provisional Application No. 62/544,461, filed Aug. 11,2017, which are all hereby incorporated by reference in their entirety.

FIELD OF INVENTION

Some embodiments relate to the application of using aerial datacollection and decision support system for traffic incident management.More specifically, some embodiments focus on using the video and dronepositioning data collected from UAV or drone flights to recreate the 3Dmodel of the incident sites. Some embodiments also describes the methodsof using the generated 3D model to generate incident reports, creatingrealistic virtual environment for training. Data management, accesscontrol, and usage charging methods are also described herein.

BACKGROUND

Incidents refer to non-recurrent traffic-impeding events such asaccidents, road spills, stalled vehicles, and etc. This UAV-basedplatform belongs to the applications of using video and images forsurveying and monitoring of roadway traffic and events.

SUMMARY OF THE INVENTION

Some embodiments provide a detailed UAV or Drone-based traffic incidentsite investigation and management support system design to providesensing, data analytics, data dissemination, decision support, andtraining materials for incident response and management agencies. Theaforementioned embodiments create an integrated drone-based hardware,software, cloud, and management solutions.

One UAV-based incident data collection and management support systemfocuses on a video-based fast 3D reconstruction with high-resolution(4k) video of incident scene with a continuous unstopped flightpatterns. The platform can be used to significantly accelerate the sitedata collection, the incident information dissemination, and trafficincident management operations. Said system can improve the dataquality, efficiency, safety, integrity, and communication in trafficincident.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 presents the architecture of one embodiment of a drone-basedincident investigation and management support system.

FIG. 2 presents an embodiment of a video-based 3D reconstruction method.

FIG. 3 presents an embodiment of traffic management support methods.

FIG. 4 presents an embodiment of Flight Path Planning and ControlMethods.

FIG. 5 presents an embodiment of UAS-based Incident Traveler InformationService Methods.

FIG. 6 presents an embodiment of UAS-based Accident Site Data AnalyticMethods.

FIG. 7 presents an embodiment of 3D Infrastructure Data Pre-Collectionand Site Positioning Methods.

FIG. 8 presents an embodiment of 3D Model Data Access Management,Control and Charging Schemes.

FIG. 9 presents an embodiment of 3D Model based Incident ManagementTraining and Educational Methods.

DETAILED DESCRIPTION I. Legend to the Drawings

FIG. 1:

-   -   101: UAS incident data collection platform    -   102: UAS ground control station    -   103: Incident information for traveler information services    -   104: Incident data analytic and training center    -   105: Incident operations with Traffic Management Centers (TMCs)    -   106: In-Vehicle On-board Devices that can receive the onsite        incident information broadcasting    -   107: Cloud traveler information services such as WAZE, Inrix,        and Google Traffic    -   108: Video-based 3D incident modeling methods    -   109: Data Visualization and Management Interface    -   110: Traffic Incident Management Training

FIG. 2:

-   -   201: Incident occurrence    -   202: Incident alerts    -   203: Traveler self-recording interface    -   204: Autonomous flight patterns    -   205: Autonomous survey patterns    -   206: Video streaming services to the cloud analytic server    -   207: Autonomous return flights    -   208: Drone flight log extraction    -   209: Video frame extraction    -   210: Video frame positioning with flight log GPS records    -   211: Frame-by-frame feature filtering and matching    -   212: Video- and Photogrammetry    -   213: Generated 3D Model of the incident site

FIG. 3:

-   -   301: Traffic Management Center (TMC)    -   302: Remote video monitoring of the incident scene    -   303: Traveler information service (511) temporary video feeds    -   304: Onsite UAV ground station for flight planning, control, and        wireless communication    -   305: Incident occurrence    -   306: Highway service patrol (HSP) response crew    -   307: UAV Flight Planning at the incident site    -   308: UAV deployment and the establishment of onsite video feed    -   309: TMC detects incidents through cellphone calls or traffic        detectors    -   310: TMC coordination with HSP vehicles and crews    -   311: TMC feedback on UAV flight and observation positions    -   312: Onsite video transmission through wireless communication        and remote UAV path and camera angle adjustment

FIG. 4:

-   -   401: UAV Automated Flight Path    -   402: UAV Takeoff and landing site    -   403: Temporary Traffic Diversion and Detouring Paths    -   404: Objects onsite that may affect the autonomous flight path        design    -   405: Through traffic detoured or affected by the incident    -   406: Incident Response Crew including police, medical, and        highway service patrol crew    -   407: UAV ground station for flight control and video quality        monitoring

FIG. 5:

-   -   501: Incident data collected from the site by UAS and response        crew    -   502: Onsite Traveler Information Support    -   503: Cloud-based Information Exchange    -   504: Users    -   505: Sending Support Information    -   506: Dedicated Wireless Communication    -   507: Event Submission Interface    -   508: Uploading Incident Related Information    -   509: Obtaining Real-time Traffic Information    -   510: Cloud-based traveler information services

FIG. 6:

-   -   601: UAS-based Accident Site Data Analytic Methods    -   602: Accident Site 3D Model Visualization and Site Measurement        Tools    -   603: Crash Damage Assessment and Reconstruction Methods    -   604: High-resolution site report generation methods    -   605: 3D visualization interface    -   606: Site measurement tools    -   607: Undamaged 3D model building    -   608: Volume differencing methods    -   609: Damage Estimation    -   610: Report of reconstructed 3-D Model    -   611: Report of other related crash site information    -   612: High-resolution 3-D model    -   613: HR Line    -   614: HR Area    -   615: HR Volume    -   616: HR Object Model

FIG. 7:

-   -   701: Pre-collected 3D Infrastructure Model    -   702: 3D infrastructure site data    -   703: Traffic light    -   704: Traffic Sign    -   705: Light Pole

FIG. 8:

-   -   801: 3D Data of the Reconstructed Incident Site Models    -   802: Data Access Control    -   803: Establishment of “clean chain of custody” of the data    -   804: User Charging: Site-based Credits System    -   805: User Tiering    -   806: Tier-based Data Accessibility    -   807: Drone-to-Ground    -   808: Ground-to-Cloud    -   809: Cloud-to-Server/UI    -   810: Server/UI-to-Customer    -   811: Cost-based charging pricing    -   812: On-demand charging payment    -   813: Discounting Method    -   814: Expedited Charging Method

FIG. 9:

-   -   901 Incident Site Reconstruction-based Crew Management Training        Methods    -   902 Onsite Shadowing    -   903 Onsite task assignment and coordination    -   904 Onsite safety training    -   905 Onsite activity sequence planning and path selection    -   906 Measurement and reporting training    -   907 Multi-Personnel and Inter-Agency coordination and        communication    -   908 Area restriction awareness and risk assessment    -   909 Communication with traffic management center regarding        onsite conditions    -   910 Incident Management Onsite Activity and Resource Management        Training Methods    -   911 Identifying and Marking Various Onsite Objects    -   912 Orchestrating site situation for resource management        training    -   913 Drone related training    -   914 Incident site screening and traffic control    -   915 Incident Management Onsite Activity and Resource Management        Training at Real Incident Traffic Scene

II. Description of Embodiments

FIG. 1 shows the entire system architecture of one embodiment. Thesystem comprises five major components. The incident data collectionplatform 101 will be deployed at the incident site and is equipped withthe drone and the high-resolution video sensors and other optionalsensors (e.g. light airborne LiDAR sensors) carried by the drone. Theground station 102 includes computers, tablets, smartphones, and/ordrone and camera controllers, the flight planning software or mobileapplication to design and upload automated flight paths to the drone,video monitoring system, and wireless communication modules to receiveand replay the video through cellular network to traffic managementcenter and data analytic cloud. The traveler information serviceinterface 103 disseminates the incident related information such aslocation, severity, traffic patterns, congestion, and detouringinformation to both the on-board devices of nearby vehicles 106 throughcellular, WiFi, or DSRC (Dedicated Short-Range Communication)connectivity and also exchange information with the cloud travelerinformation services such as WAZE, Inrix, HERE(Nokia), Google Traffic107. The data analytic and training center (104) includes a cloud-based3D model reconstruction module 108, incident reporting and analysisinterface 109, and the virtual reality (VR) based training interfaces110. The interface with Traffic Management Center (TMC) allows thecoordination between TMC operators and onsite crew and the real-timevideo collected by the system will be transmitted to TMC for monitoringthe progress of the incidents.

The incident data collection platform comprises a drone equipped with avideo system and one or more sensors. The one or more sensors mayinclude LiDAR and other sensors. The drone may include anobserver/surveying unit and an operating unit. The drone may include aU2V communication unit for communicating with traveler informationservice 103. The drone may include a flight control and communicationthat transmits and receives electronic messages from the ground station102. Drone may receive flight control signals from the ground stationthat are processed by a processor of the drone to control one or moremotors or actuators on the drone to control a flight path. A dataanalytics and training center 104 may use information from a groundstation 102. The data analytics and training center 104 may includeincident site and event extracting and processing, a visualizationinterface with survey and analysis, training, and public informationservices, and photogrammetry of LiDAR 3D model reconstruction.Photogrammetry or videogrammetry may comprise identifying locations ofpoints by comparing the locations of an object in one or more frames ofvideo. The data analytics and training center 104 may includepre-collected LiDAR and road inventory data, which may comprises datathat was previously collected using LiDAR to identify the locations ofobjects. Video frame geotagging may be used to add geotags to videoframes and image geotagging may be used to add geotags to images.

FIG. 2 depicts the methods of generate the 3D model of the incident sitefrom the video data. Once the incident occurred 201, travelers canreport the incidents to cloud services to alert TMCs 203. User input iscollected from user components. A signal is sent to the UAV about theincident 202. The incident alerts 204 will be evaluated and confirmed byTMC and response crew will be sent to the incident site to start thedata collection. There are several methods in this process. The firstmethod is the automated flight path design and autonomous flight whichincludes the flight from the takeoff/landing site to the incidentlocation 204, a software package that defines the autonomous incidentsurvey patterns based on the user entered incident location and POI(Point of interests) information 205. The UAV performs a survey aroundthe incident site, which may include a 360 degree flight around theincident site. The collected video along with flight log data(containing coordinates) will be transmit to the cloud services 206 toconduct the 3D reconstruction modeling. The video is collected by thevideo system on the UAV and transmitted using, for example, a wirelesstransmission method. The UAV then returns to its base. The flight willbe concluded with automated landing process 207. Once the video is inthe cloud server, the detailed video and flight log processing willstart. A software package will first process and decode the flight logs208. Different decoders will be developed for different brands ofdrones. Video frame will be extracted along with its timecode data 209.The extracted video frame will then be matched with the drone locationat the time of the video frame interpolated from the flight logs. Thenthe video frames from adjacent time intervals will be matched togetherto find common features. Consecutive frames are examined to matchfeatures common to the consecutive frames to identify a featurecorresponding to a feature in the real world. A feature motion trackingwill be conducted to rapidly match features cross consecutive videoframes 211. Features may be identified using descriptors, such as SIFT,and other artificial intelligence methods, such as neural networks. Thena video or photogrammetry procedure will be conducted 212. The matchedfeatures are then processed through triangulation and 3D reconstructingprocess to be vertexes of the 3D model. Triangulation identifiescoordinates of a feature in 3D space by using a plurality of signalpoints, which may correspond to a plurality of frames of video. Then themesh and surface of the 3D model of the incident site will be generated.A mesh is generated from the triangulated points. Then surface smoothingis performed on the mesh. The end result is the created 3D model of theincident site 213.

FIG. 3 shows the communication methods between the onsite platform andTMC. The communication include the remote monitoring at TMC 301 andonsite monitoring at the incident sites. Remote monitoring may beperformed by human users or by automated systems, such as neuralnetworks. Once an incident occurs 305, the event may be detected by TMC309 with cellphone calls, CCTV monitoring, or traffic detectors. The TMCmay detect the incident by monitoring for spikes of activity or one ormore features characteristic of an incident. Highway service patrol(HSP) crew with the drone platform 306 can coordinate with TMC 310 andrespond to the incident scene. The drone operators from the HSP crewwill conduct initial flight planning 307. Communication can beestablished between TMC and drone team 311 to make adjustment to theflight patterns. Then the drone will be deployment and start collectingvideo data 308. The drone video will be transmitted to the groundstation 304. The work stations, tablets, or smartphones at the groundstation 304 will use the cellular or WiFi connections to relay the videoto the TMCs 312 for displaying in the video walls to monitor theincident scene remotely 302. TMC can publish the temporary Adhoc trafficvideo of the incident through traveler information website e.g. 511website if deemed publishable 303.

FIG. 4 shows the basic flight path planning and control methods. Beforethe flight, flight path planning (401) needs to be designed with userinputs of surrounding environments including site geometry, ground crewlocations (406), risk and distraction level of drivers. In anembodiment, the flight path is generated automatically from these inputsbased on a flight path planning algorithm. Meanwhile, the Detour Path(403) is determined by pre-populating both static infrastructure anduser-input onsite objects (404) and restrictions for any changes to theflight planning software. User-input onsite objects may be received fromuser interface components and may be provided by data indicating theexistence and location of one or more objects at the incident site.During the flight, the UAV first take off from the Takeoff Site (402),and then the distraction reduction is achieved by rising UAV quickly andaway from lines of sight of through traffic (405). The flight pathplanning algorithm may automatically determine the appropriate flightpath to move the UAV quickly and away from lines of sight of drivers ator near the incident location. The ground station (407) will implementedsoftware package that continuously checks on glares and image stability,and automatically adjust the speed and position of UAV to optimize thevideo quality. The ground station may transmit one or more controlsignals to the UAV to control the UAV's movements as described.

FIG. 5 shows the UAS-based incident traveler information service methods501 which comprises of onsite traveler information support 502 andcloud-based information exchange 503. The onsite traveler informationsupport 502 sends support information 505 such as incident information,diversion alerts, images of site and congestion etc. to users 504through dedicated wireless communication 506 such as by text, image, orsound notification. The cloud-based information exchange 503 enablesuploading and updating incident related information 508 to cloud-basedtraveler information services 510 such as Google maps, Waze, etc.through event submission interface 507, and it also enables obtainingreal-time traffic information 509 from 510 to determine the potentialtravel recommendations.

FIG. 6 shows the UAS-based accident site data analytic methods 601comprising of accident site 3-D model visualization and site measurementtool 602, crash damage assessment and reconstruction methods 603,high-resolution site report generation methods 604. 602 provides a 3-Dvisualization interface 705 for the site measurement tool 706. 703consists of undamaged 3-D model building 607. In an embodiment, anundamaged 3D model is built of each vehicle involved in the event at theincident site, where the 3D model represents the vehicle in itsundamaged state. Based on 607, a point cloud volume differencing method708 is implemented to calculate the volume loss and evaluate thepotential damaged vehicle components 609. The point cloud volumedifferencing method 708 compares the 3D models as generated after theeven with the corresponding undamaged 3D models. The difference involume between the corresponding pairs of models is calculated. 604generates a report of reconstructed 3-D model 610 which consists of highresolution 3-D model 612 including high-resolution (HR) line 613, HRarea 614, HR volume 615, and HR object model 616. 604 also generatesreport of other related crash site information 611 based on otherenvironmental information.

FIG. 7 shows that pre-collected 3D infrastructure model 701 containingstatic infrastructure elements, such as lane marking, traffic light 703,traffic sign 704, and light pole 705. Site infrastructure data collectedin real time will be processed to match with pre-collectedinfrastructure model through positioning and rescale. The staticinfrastructure model and site infrastructure model will be combined torecreate contextual view of the incident and visualization.

FIG. 8 shows the 3-D model data access management control and chargingschemes 801. 901 comprises of data access control method 802,establishment of “clean chain of custody” of the data 803, and the usercharging system 804. The data access control method 802 classifies thepotential users to different tiers 805, and grants differentaccessibility of the details of the data to different tiers of users806. 803 secures the data transferring between the system componentssuch as drone-to-ground 807, ground-to-cloud 808, cloud-to-server/UI809, and server/UI-to-customer 810. 804 includes a cost-based chargingpricing method 811 to determine the price per onsite data collectioncomplexity as well as the data transferring and processing complexity.804 also includes an on-demand charging payment component 812 whichallows users pay in diversified ways such as Pay-per-site-area,Pay-per-accident-severity, Pay-per-report-details, subscription type,etc. 804 also includes a discounting method 813 and an expeditedcharging method 814 to determine the charging discount and potentialadditional fee for expedited services.

FIG. 9 illustrates the training and educational methods with thegenerated 3D incident site model. The methods include two majorcategories, one is incident site reconstruction-based crew managementtraining methods 901, the other is incident management onsite activityand resource management training methods 910. Reconstruction-based crewmanagement training methods will load the 3D models of incident sitesinto VR(Virtual Reality), AR (Augmented Reality) or CAVE (cave automaticvirtual environment) or 360 video environment for trainees to experienceand practice. Trainees can perform onsite shadowing in which traineefollows an onsite crew to investigate and mimic their activities in theactual site as recorded by onsite video 1002, onsite task assignment andcoordination training 1003, onsite safety training 1004, onsite activitysequence planning and path selection 1005, measurement and reportingtraining 1006, multi-group interagency coordination and communication1007, the onsite functional area awareness and risk assessment andmitigation training 1008, and the communication training with trafficmanagement center regarding onsite conditions 1009. Incident siteresource management training methods 910 can be conducted by establishthe details of incident site in a VR, AR, CAVE, or 360 video environmentto train the recognition of critical onsite objects 1011, resource (e.g.police and emergency vehicles, workstations, measurement and surveyequipment) allocation and positioning training 1012, site screening andtraffic control training 1013, and drone planning and operationstraining including the playback of drone trajectories, drone safetyassessment and risk mitigation, 1013.

The following exemplary embodiments represent some systems and methodsthat may be used consistent with the embodiments described above.

-   1. A UAV-based incident site investigation and management support    system consists of the follow platforms and subsystems    -   a. Flight planning and mission control platform for flight path        design to execute safe and efficient autonomous surveying path        around an incident site;    -   b. UAV-based high-resolution video and location sensor system to        collect site data for 3D reconstruction;    -   c. 3D modeling and analytic system based on videogrammetry with        high-resolution location-tagged video frames to create the 3D        reconstruction of the incident sites    -   d. Data dissemination and communication system for real-time        receipt of incident site information and transmission to traffic        management center or traveler information system,    -   e. Visualization, reporting, and training platforms based on the        generated 3D model of the incident site,-   2. An incident site reconstruction Drone Video-based Incident Site    Investigation Methods comprising of the following key computational    steps    -   a. high-resolution (4k+) video frame extraction,    -   b. video frame-by-frame geotagging with the GPS flight logs from        the drone,    -   c. frame-to-frame common feature extraction and matching,    -   d. feature triangulation and 3D reconstruction,    -   e. mesh building and surface smoothing,    -   f. onsite 3D model preview with coarse resolution,    -   g. cloud-based 3D model reconstruction for high-resolution        modeling.-   3. The method of conducting remote video monitoring of the incident    site from the traffic management centers (TMCs) through the UAV    incident site investigation and management support system of    enabling two-way communication methods between TMCs and incident    site as follows    -   a. Uplink to TMC method of transmitting the incident site video        collected by UAVs to TMC video wall through a video relaying        services established at the ground station with cellular        communication    -   b. Downlink back to incident site methods of establishing        control command to allow TMC operators to conduct pan-tilt-zoom        actions on the UAV and its cameras to monitor critical locations        and spots at the site.-   4. A flight path planning and control methods for UAV incident site    investigation to ensure safety, efficiency, and data quality    comprising of flight planning, flight control, object avoidance,    distraction reduction, and in-flight video quality assessment and    control.-   4.1 The methods of flight path planning that customizes flight    patterns to 1) cover the entire site based on site geometry that may    affect the flight contour and lines of sight restrictions; 2) to    establish standard the takeoff and landing places, flight heights,    and paths to avoid static and moving objects and people onsite but    to allow complete and clear data collection; 3) to establish    sufficient escaping room in the pattern to mitigate potential risk    and liability of the flight so that the paths avoid hitting people,    high-priced equipment etc. even during drone failures; 4) to adjust    the fly speed, altitude and position to minimize the distraction    level of drivers to reduce the impact to traffic on the same and    opposing directions.-   4.2 An object avoidance method of avoiding objects such as trees and    infrastructures by pre-planning the detailed flight path points by    pre-populating both static infrastructure and user-input onsite    objects and restrictions for any changes to the flight planning    software for calculating 3D trajectories to pass safely through    those objects;-   4.3 A distraction reduction method that minimizes the distraction of    drivers on both traveling lanes and the opposing lanes by    pre-planning the flightpath that rises quickly and away from view of    drivers in high-speed vehicles (e.g. opposing or thru traffic).-   4.4 The methods of in-flight assessing and controlling of video    quality, as described in claim 2b, including the glare detection and    adjustment of camera angles or flight paths and the image stability    assessment and the adjustment of speed and position to stabilize the    video scene.-   5. UAS-based incident traveler information service methods of    disseminating incident information to nearby vehicles through their    onboard connected vehicle devices and exchanging information with    cloud traveler information services.-   5.1 The onsite traveler information support method of sending    incident information, diversion alerts, images of site and    congestion etc. to travelers in ways of text, image, and voice    notification based on the data collected by onsite UAS through    connected vehicle communication (DSRC) or other wireless    communication such as 4G-LTE and Wi-Fi.-   5.2 The methods of exchanging information between UAS and    cloud-based traveler information services include both the uplink    and downlink methods as follows    -   a. Uplink: uploading and updating the incident related severity,        congestion, and diversion information to cloud-based traveler        information services through event submission interfaces    -   b. Downlink: obtaining real-time traffic information from those        cloud services to determine the potential travel recommendations        for the incident or accidents including the optimal diversion        routes, diversion locations upstream, and the impact road        network.-   6. The methods of visualizing, analyzing, and conducting traffic and    activity simulation with the reconstructed 3D incident site model.-   6.1 The methods of visualizing 3D model of accident site and    measuring site includes a 3D visualization interface and measurement    tools on 3D models as follows    -   a. The 3D visualization interface of the entire accident site        within the background of colored 3D LiDAR model pre-collected as        described in claim 7.1 or street-view environment.    -   b. Site measurement tools, including line, area, surface,        comparison, etc., that can measure tire marks, distance, vehicle        geometry, impact area, volume, surface curvature to assess        incident damages.-   6.2 The methods of crash damage assessment and reconstruction    Methods    The methods of assessing the severities and damaged components of    the crashed vehicles based on 1) the undamaged 3D model building    with the dimensions obtained for the vehicle models involved, 2) the    volume differencing methods to comparison and visualize between the    reconstructed 3-D model of the vehicles and the undamaged models,    and 3) calculation of the volume loss and affected potential vehicle    components at the damage location.-   6.3 The methods of generating the incident site data with the    reconstructed 3D model with measurement tools for the followings    -   a. High-resolution line with point-to-point data in 3-D point        cloud;    -   b. High-resolution area enabling drawing and selecting points to        set the are boundary in 3-D point cloud;    -   c. High-resolution volume showing undamaged object template as        3-D point cloud; d. High-resolution object model combined with        static 3-D infrastructure model as described in claim 8;    -   e. Other environmental data such as weather, surrounding        environment, driver/roadside view of collision, illumination,        etc. by integration of detected video image scenery information        with other environment data sources (e.g. weather, sun position        etc.).-   7. The methods of establishing static 3D Infrastructure model and    post-processing the reconstructed 3D incident site model to the    scale and accurate geographical position.-   7.1 The static 3D infrastructure data collection and modeling    methods wherein said using pre-collected corridor data consisting of    Mobile LiDAR to establish corridor static 3D Model and create    geospatial indexes of critical infrastructure objects (mileposts,    light poles, overhead gantries, traffic sings/signals) for further    positioning site recreation purpose.-   7.2 3D Infrastructure site data positioning and overlaying methods    of repositioning and rescaling of the reconstructed 3D model by    matching the infrastructure features within the 3D model with the    static 3D model, and merging the 3D model with the pre-processed 3D    static infrastructure model to create the comprehensive view with    upstream, downstream and surrounding scenes for the incident    modeling and visualization.-   8. The methods of managing the access, maintaining the chain of    custody, and charging schemes for the reconstructed 3D Model Data.-   8.1 The method of controlling the access of the 3-D model data by    different limitation of delivering data between different tiers of    users regarding the accessible level of details:    -   a. Tier 1 users including Public Safety Department,        Transportation Safety/Management Agencies, and Incident Response        Team who can get access of the High-resolution 3-D models with        full details for reporting, analysis, and training;    -   b. Tier 2 users including Insurance/Medical Companies who can        get access of the report and 3-D model of detailed damaged        vehicle parts/infrastructures for damage/liability assessment;    -   c. Tier 3 users including travelers involved in crash who can        get access of the detailed damage reports, 3D view of incident        site, and images for insurance claims and legal disputes;    -   d. Tier 4 users including other travelers, data analytic agency        departments and consulting companies who can get access of        anonymized, aggregated data and crash reports, per        request/purchase.-   8.2 The method of controlling the distribution of 3-D model data by    the establishment of a “clean chain of custody” between data    transferring nodes to ensure the data security and track the whole    process of data transferring and retrieving as:    -   a. Drone-to-Ground: A encrypted data transferring link between        UAS and ground units with dedicated communication frequency and        frequency jump technique;    -   b. Ground-to-Cloud: A secured and encrypted data flow through        cellular/Wi-Fi communication with digital        signature/authentication and data validation;    -   c. Cloud-to-User Server/Interface: A private password-protected        access-controlled interface to ensure the security of data and        reports;    -   d. User Server/Interface-to-Customer: A computer/server        authorization process at the time of software installation.-   8.3 The method of managing the user charge comprising of:    -   a. Cost-based pricing tiers based on        -   the cost of onsite collection per the data volume, site            area, computational level, and time sensitivity;        -   the cost of data/report retrieval per the data volume,            accuracy and level of details needed by users, insurance            companies, medical providers, and other agents.    -   b. On-demand charging payment style including Pay-per-site-area,        Pay-per-accident-severity, Pay-per-report-details, subscription        type, etc.    -   c. Payment discounting method such as user-type-discount,        subscription length discount, bulk credit purchasing, incident        severity, etc.    -   d. Expedited charging method of charging an additional        processing fee for expedited services required by users.-   9. The methods of training of incident response crew members and the    resource management during incidents by integrating the    reconstructed 3D incident site into the virtual reality environment    and the recorded onsite activities to create the high-resolution    immersive and interactive view of the incident site.-   9.1 The methods of training incident response crew members by the 3D    model to create a immersive visualization of an actual incident site    and building a virtual reality training environment to learn onsite    task assignment, reporting and measurement, onsite safe/efficient    movement, shadowing with actual response crew's activities, the    coordination and communication among team members, among agencies,    and with TMCs, personnel requirement for area access at the site,    etc.-   9.2 The methods of incident management onsite resource management    training method will utilize various onsite objects, including    vehicles, tape measure, rescue vehicles, other equipment to assist    officer in training to efficiently cope with incidents regarding    site-staging, plan drone operations takeoff/landing site, drone    flight path replay, drone safety training, traffic diversion and    staging systems.

1. A system for generating incident site data based on a reconstructed3D model of the incident site and input data from one or moremeasurement tools, the system comprising one or more processorsconfigured to perform the operations of: a. causing generation of thereconstructed 3D model based on one of more object models of objectsaccording to at the incident site and a current state of the objects; b.causing generation of the incident site data based on: (i) receipt of ahigh resolution (HR) line with point-to-point data, in 3D point cloud,selected with the one or more measurement tools being applied to aninterface displaying a visualization of the reconstructed 3D model; (ii)receipt of a high resolution (HR) area based on a drawn boundary andselected points, in 3D point cloud, selected with the one or moremeasurement tools being applied to the interface; (iii) generation of ahigh resolution (HR) volume based on an undamaged object template in 3Dpoint cloud; (iv) generation of a high resolution (HR) object modelbased on a static 3D infrastructure model; and c. identifyingdifferences between volumes in the reconstructed 3D model and volumes inthe high resolution (HR) object model to identify damage at the incidentsite.
 2. The system of claim 1, wherein the operation of generating theincident site data further comprises the operation of: processingenvironmental data comprising one or more of: weather data, surroundingincident site environment data, collision roadside view data, collisiondriver view data, scenery data and illumination data.
 3. The system ofclaim 1, wherein the measurement input data comprises incident damagedata from the one or more measurement tools being applied to at leastone of: a tire mark, distance, vehicle geometry, impact area, volume,surface curvature in the interface displaying the visualization of thereconstructed 3D model.
 4. The system of claim 1, wherein generation ofthe high resolution (HR) object model based on a static 3-Dinfrastructure model comprises the operations of: generating the static3D infrastructure model; generating a 3D model based on thehigh-resolution (HR) volume from one or more undamaged object templates,as 3D point cloud, representing an undamaged state of one or moreobjects at the incident site; and generating the high resolution (HR)object model by merging the static infrastructure model with the 3Dmodel.
 5. The system of claim 4, wherein generating the static 3Dinfrastructure model comprises the operations of: accessingpre-collected infrastructure element data, the infrastructure elementdata comprising Mobile LiDAR-based geospatial indices for at least oneof: a lane mark, a traffic sign, a light pole, a milepost, a trafficsignal, an overhead gantry and a road object; matching respectivepre-collected infrastructure element data with infrastructure objects atthe incident site; and generating the static 3D infrastructure modelbased in part on the matched respective pre-collected infrastructureelement data.
 6. The system of claim 1, wherein causing generation ofthe reconstructed 3D model based on one of more object models of objectsaccording to at the incident site and a current state of the objectscomprises the operation of: creating one or more multi-resolution 3Dmodels for different preview, site inspection and viewing, and sitesurvey and measurement applications and different pricing levels byextracting and geotagging video frames at different rates.
 7. The systemof claim 6, further comprising the operations of: performing uplink to atraffic management center (TMC) by transmitting a video of the incidentsite collected by a drone to a video wall at the TMC, the uplink beingthrough a video relaying service provided at a ground station withcellular communication or fiber communications; performing uplinkthrough edge devices (includes smartphone, tablets, laptops, or edgecomputers) to a video streaming service through cellular or fibercommunications then transmitted to the traffic management center (TMC);and performing downlink communications from the traffic managementcenter (TMC) by establishing voice commands/communication through thedrone or onsite emergency vehicles' microphone/speaker systems.
 8. Thesystem of claim 6, further comprising the operations of: flight planningfor the drone to determine a flight plan, wherein the flight planensures a safety of onsite crew and traffic; optimizing accuracy andefficiency of onsite data collection; and reducing distraction ofdrivers in an environment external to the drone.
 9. The system of claim6, further comprising the operations of: performing Point-of-Interests(POI) orbits of multiple altitude levels centered around an accidentsite; and allowing operators to draw the perimeters of accident sites,site crew activities, traffic control plan, and key obstacles on asatellite-map-based interface for designing flight path.
 10. The systemof claim 6, further comprising the operations of: detecting glare tomitigate the glare by optimizing a camera exposure setting to maximize avisibility of vehicle and lane marking edges; and removing the glare byinstalling and adjusting an angle of polar lenses to the drone camerasbased on a relationship between flight paths and a sun direction. 11.The system of claim 6, further comprising the operations of:crowdsourcing onsite and traffic conditions through feedbacks fromoperators and travelers around the incident site by allowing uploadingof condition reports, pictures of the incident site and traffic, andother user-reported information.
 12. The system of claim 6, furthercomprising the operations of: receiving uploaded information about anincident severity, traffic congestion, and traffic diversion to acloud-based or TMC-server-based traveler information service through anevent submission interface.